Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access
  • Access by Xinjiang University

First-passage time in space-dependent stochastic resetting

Johannes Aspman1, Daniel Mastropietro1,2,3, and Jakub Mareček1

Phys. Rev. E 114, 024110 – Published 7 August, 2026

DOI: https://doi.org/10.1103/w2yj-91m8

Abstract

We consider the mean first-passage time (MFPT) through a target of interest for a diffusive particle of Langevin type, with the added condition that the particle is reset to its original position with some rate r. We study both smooth and nonsmooth, nonconvex potentials, focusing on the case where the reset rate depends on the space coordinate. For quadratic and piecewise-quadratic potentials, we show that the benefits of resetting depend on the ratio between drift and noise, and become more important as the drift potential becomes smaller compared to the noise. When the target is a local optimum of the potential, we further show that it is beneficial to use a space-dependent resetting where the reset rate is lower when the particle is closer to the target.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (42)

  1. I. Eliazar, T. Koren, and J. Klafter, Searching circular DNA strands, J. Phys.: Condens. Matter 19, 065140 (2007).
  2. Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature (London) 521, 436 (2015).
  3. Martin R. Evans and Satya N. Majumdar, Diffusion with stochastic resetting, Phys. Rev. Lett. 106, 160601 (2011).
  4. M. R. Evans and S. N. Majumdar, Diffusion with optimal resetting, J. Phys. A: Math. Theor. 44, 435001 (2011).
  5. M. R. Evans and S. N. Majumdar, Diffusion with resetting in arbitrary spatial dimension, J. Phys. A: Math. Theor. 47, 285001 (2014).
  6. O. Bénichou, M. Moreau, P.-H. Suet, and R. Voituriez, Intermittent search process and teleportation, J. Chem. Phys. 126, 234109 (2007).
  7. E. Gelenbe, Search in unknown random environments, Phys. Rev. E 82, 061112 (2010).
  8. S. Janson and Y. Peres, Hitting times for random walks with restarts, SIAM J. Discrete Math. 26, 537 (2012).
  9. A. Pal and S. Reuveni, First passage under restart, Phys. Rev. Lett. 118, 030603 (2017).
  10. É. Roldán and S. Gupta, Path-integral formalism for stochastic resetting: Exactly solved examples and shortcuts to confinement, Phys. Rev. E 96, 022130 (2017).
  11. M. R. Evans, S. N. Majumdar, and G. Schehr, Stochastic resetting and applications, J. Phys. A: Math. Theor. 53, 193001 (2020).
  12. S. Ahmad, I. Nayak, A. Bansal, A. Nandi, and D. Das, First passage of a particle in a potential under stochastic resetting: A vanishing transition of optimal resetting rate, Phys. Rev. E 99, 022130 (2019).
  13. A. Pal and V. V. Prasad, Landau-like expansion for phase transitions in stochastic resetting, Phys. Rev. Res. 1, 032001(R) (2019).
  14. S. Ray, D. Mondal, and S. Reuveni, Péclet number governs transition to acceleratory restart in drift-diffusion, J. Phys. A: Math. Theor. 52, 255002 (2019).
  15. S. Ray and S. Reuveni, Diffusion with resetting in a logarithmic potential, J. Chem. Phys. 152, 234110 (2020).
  16. O. L. Lauber Bonomo and A. Pal, First passage under restart for discrete space and time: Application to one-dimensional confined lattice random walks, Phys. Rev. E 103, 052129 (2021).
  17. S. Ahmad, K. Rijal, and D. Das, First passage in the presence of stochastic resetting and a potential barrier, Phys. Rev. E 105, 044134 (2022).
  18. M. Luby, A. Sinclair, and D. Zuckerman, Optimal speedup of Las Vegas algorithms, Inf. Process. Lett. 47, 173 (1993).
  19. H. Alt, L. Guibas, K. Mehlhorn, R. Karp, and A. Wigderson, A method for obtaining randomized algorithms with small tail probabilities, Algorithmica 16, 543 (1996).
  20. H. Kautz, E. Horvitz, Y. Ruan, C. Gomes, and B. Selman, Dynamic restart policies, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 97 (AAAI, 2002), p. 674.
  21. H. Tong, C. Faloutsos, and J.-Y. Pan, Random walk with restart: Fast solutions and applications, Knowl. Inf. Syst. 14, 327 (2008).
  22. O. V. Shylo and O. A. Prokopyev, Restart Strategies (Springer International Publishing, Cham, 2018), pp. 205–220.
  23. H. Yu, S. Yang, and S. Zhu, Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning, Proc. AAAI Conf. Artif. Intell. 33, 5693 (2019).
  24. I. Loshchilov and F. Hutter, SGDR: Stochastic gradient descent with warm restarts, in International Conference on Learning Representations (ICLR, 2017).
  25. Y. Bae, Y. Song, and H. Jeong, Stochastic restarting to overcome overfitting in neural networks with noisy labels, Mach. Learn.: Sci. Technol. 6, 015062 (2025).
  26. M. Gagliolo and J. Schmidhuber, Learning restart strategies, In Proceedings of the 20th International Joint Conference on Artifical Intelligence (Morgan Kaufmann Publishers Inc., San Francisco, CA, 2007), pp. 792–797.
  27. N. A. Wedge and M. S. Branicky, On heavy-tailed runtimes and restarts in rapidly-exploring random trees, in Twenty-Third AAAI Conference on Artificial Intelligence (AAAI, 2008), pp. 127–133.
  28. V. Roulet and A. d'Aspremont, Sharpness, restart and acceleration, Advances in Neural Information Processing Systems, Vol. 30 (NIPS) (Curran Associates, Inc., 2017).
  29. J.-H. Lorenz, Restart strategies in a continuous setting, Theory Comput. Syst. 65, 1143 (2021).
  30. J. Renegar and B. Grimmer, A simple nearly optimal restart scheme for speeding up first-order methods, Found. Comput. Math. 22, 211 (2022).
  31. D. Starkov and S. Belan, Universal performance bounds of restart, Phys. Rev. E 107, L062101, (2023).
  32. D. Davis, D. Drusvyatskiy, S. Kakade, and J. D. Lee, Stochastic subgradient method converges on tame functions, Found. Comput. Math. 20, 119 (2020).
  33. J. Berner, P. Grohs, G. Kutyniok, and P. Petersen, The Modern Mathematics of Deep Learning, edited by P. Grohs and G. Kutyniok (Cambridge University Press, Cambridge, 2022), pp. 1–11.
  34. A. Pal, Diffusion in a potential landscape with stochastic resetting, Phys. Rev. E 91, 012113 (2015).
  35. S. Redner, A Guide to First-Passage Processes (Cambridge University Press, Cambridge, 2001).
  36. D. Mastropietro, G. Korpas, V. Kungurtsev, and J. Marecek, Parallel variational quantum algorithms with gradient-informed restart to speed up optimization in the presence of barren plateaus, IEEE Trans. Quantum Eng. 7, 1 (2026).
  37. Wolfram Research, Inc., Mathematica, Version 13.1, Champaign, IL (2022).
  38. F. Batola, Une généralisation d'une formule d'Erdelyi-Tricomi, Arkiv för Matematik 20, 87 (1982).
  39. F. Tricomi, Sulle funzioni ipergeometriche confluenti, Ann. Mat. Pura Appl. 26, 141 (1947).
  40. W. Mou, N. Flammarion, M. J. Wainwright, and P. L. Bartlett, Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity, Bernoulli 28, 1577 (2022).
  41. Y. Nesterov, A method for unconstrained convex minimization problem with the rate of convergence o(1/k2̂), Dokl. Akad. Nauk. SSSR 269, 543 (1983).
  42. D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in International Conference on Learning Representations (ICLR, 2015).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation